Image segmentation refers to the process to divide an image into meaningful non-
overlap** regions according to human perception, which has become a classic topic since …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Interactive segmentation allows users to extract target masks by making positive/negative
clicks. Although explored by many previous works, there is still a gap between academic …

Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …

The existing binary foreground map (FM) measures to address various types of errors in
either pixel-wise or structural ways. These measures consider pixel-level match or image …

Recent works on click-based interactive segmentation have demonstrated state-of-the-art
results by using various inference-time optimization schemes. These methods are …

Deep neural networks have become a mainstream approach to interactive segmentation. As
we show in our experiments, while for some images a trained network provides accurate …

Interactive object selection is a very important research problem and has many applications.
Previous algorithms require substantial user interactions to estimate the foreground and …

An interactive image segmentation algorithm, which accepts user-annotations about a target
object and the background, is proposed in this work. We convert user-annotations into …